INFO-H-500 Image Acquisition & Processing
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  • 1.Introduction
    • Biological vision
    • Image sensors
    • Image representation
  • 2.Low-level image processing
    • Histogram
    • Linear filtering
    • Rank based filters
    • Image restoration
    • Edge detection
  • 3.Image segmentation
    • Typical image processing pipelines
    • Histogram based segmentation
    • Border based segmentation
    • Region based segmentation
    • Model based segmentation
      • Model based segmentation
      • Active contours
      • Hough transform
    • Examples
  • 4.Morphomathematics
    • Morphomathematical operators
    • Combined operations
    • The watershed transform
    • Gray level morphology
  • LABS
  • References
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In [1]:
%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
In [2]:
from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
In [3]:
# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [4]:
# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
In [5]:
# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
In [6]:
im = data.page()

bg = skr.median(im, disk(10))

res = (1.*im/bg) < .8

plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
In [7]:
# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100

plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
In [8]:
from skimage.feature import canny


ca = canny(im)

plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
In [9]:
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1

#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))

ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead.
  def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
Out[9]:
<matplotlib.image.AxesImage at 0x7f233eb0cdd0>
In [10]:
im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [11]:
# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
In [12]:
# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [13]:
from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
Out[13]:
In [14]:
# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [15]:
# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [16]:
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries

gr = skr.gradient(im,disk(3))

local_min = im <= skr.minimum(im,disk(5))

lab = label(local_min)

#med = skr.median(im,disk(5))

ws = watershed(gr,lab)

plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))

#plt.imshow(local_min)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead.
  def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
Out[16]:
<matplotlib.image.AxesImage at 0x7f233c37ed50>
In [17]:
rgb = imread('../data/4colors.JPG')

plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
In [18]:
r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
Out[18]:
<matplotlib.image.AxesImage at 0x7f233c2dde10>
In [19]:
s = rgb.sum(axis=2)
th = s > 100

#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))

plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
Out[19]:
<matplotlib.colorbar.Colorbar at 0x7f233c524bd0>
In [20]:
lab = label(pth)

lut = np.arange(0,np.max(lab)+1)

plt.imshow(lab)
plt.colorbar()

mask = lab == 20
plt.imshow(mask)
Out[20]:
<matplotlib.image.AxesImage at 0x7f233d1c3b50>
In [21]:
from random import shuffle
shuffle(lut)
In [22]:
shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
Out[22]:
<matplotlib.colorbar.Colorbar at 0x7f23432b7410>
In [ ]:

In [23]:
# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [24]:
m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
Out[24]:
<matplotlib.colorbar.Colorbar at 0x7f233c4963d0>
In [25]:
th1 = m < 90

th2 = np.bitwise_and(110 > m,m < 130)

plt.imshow(th2)
Out[25]:
<matplotlib.image.AxesImage at 0x7f233d13a690>
In [26]:
markers = label(th2)
plt.imshow(markers)
plt.colorbar()
Out[26]:
<matplotlib.colorbar.Colorbar at 0x7f233c45ae90>
In [27]:
markers[markers==3] = 2
ws = watershed(im,markers)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead.
  def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
In [28]:
plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
Out[28]:
<matplotlib.image.AxesImage at 0x7f233c314890>
In [29]:
# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [30]:
plt.hist(im.flatten(),255);
In [31]:
from skimage.filters import threshold_otsu

t_otsu = threshold_otsu(im)
t_otsu
Out[31]:
36
In [32]:
th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
Out[32]:
<matplotlib.image.AxesImage at 0x7f233c294610>
In [33]:
lab = label(th,connectivity=1)
plt.imshow(lab)
Out[33]:
<matplotlib.image.AxesImage at 0x7f2337fe0ed0>
In [34]:
from skimage.measure import regionprops
In [35]:
props = regionprops(lab)

brain = (lab==7).astype(np.uint8)

pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))

plt.imshow(pp)
Out[35]:
<matplotlib.image.AxesImage at 0x7f2337f69290>
In [36]:
for p in props:
    print(p.area, p.label)
1459 1
5 2
1 3
3 4
1 5
16 6
6323 7
1 8
2 9
1 10
1 11
1 12
1 13
16 14
1 15
1 16
1 17
2 18
2 19
2 20
2 21
30 22
1 23
1 24
1 25
1 26
2 27
2 28
5 29
1 30
1 31
2 32
1 33
13 34
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